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Referência para ultralytics/models/yolo/detect/val.py

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ultralytics.models.yolo.detect.val.DetectionValidator

Bases: BaseValidator

Uma classe que estende a classe BaseValidator para validação com base num modelo de deteção.

Exemplo
from ultralytics.models.yolo.detect import DetectionValidator

args = dict(model='yolov8n.pt', data='coco8.yaml')
validator = DetectionValidator(args=args)
validator()
Código fonte em ultralytics/models/yolo/detect/val.py
class DetectionValidator(BaseValidator):
    """
    A class extending the BaseValidator class for validation based on a detection model.

    Example:
        ```python
        from ultralytics.models.yolo.detect import DetectionValidator

        args = dict(model='yolov8n.pt', data='coco8.yaml')
        validator = DetectionValidator(args=args)
        validator()
        ```
    """

    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
        """Initialize detection model with necessary variables and settings."""
        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
        self.nt_per_class = None
        self.nt_per_image = None
        self.is_coco = False
        self.is_lvis = False
        self.class_map = None
        self.args.task = "detect"
        self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
        self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
        self.niou = self.iouv.numel()
        self.lb = []  # for autolabelling

    def preprocess(self, batch):
        """Preprocesses batch of images for YOLO training."""
        batch["img"] = batch["img"].to(self.device, non_blocking=True)
        batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
        for k in ["batch_idx", "cls", "bboxes"]:
            batch[k] = batch[k].to(self.device)

        if self.args.save_hybrid:
            height, width = batch["img"].shape[2:]
            nb = len(batch["img"])
            bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
            self.lb = (
                [
                    torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
                    for i in range(nb)
                ]
                if self.args.save_hybrid
                else []
            )  # for autolabelling

        return batch

    def init_metrics(self, model):
        """Initialize evaluation metrics for YOLO."""
        val = self.data.get(self.args.split, "")  # validation path
        self.is_coco = isinstance(val, str) and "coco" in val and val.endswith(f"{os.sep}val2017.txt")  # is COCO
        self.is_lvis = isinstance(val, str) and "lvis" in val and not self.is_coco  # is LVIS
        self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(len(model.names)))
        self.args.save_json |= (self.is_coco or self.is_lvis) and not self.training  # run on final val if training COCO
        self.names = model.names
        self.nc = len(model.names)
        self.metrics.names = self.names
        self.metrics.plot = self.args.plots
        self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf)
        self.seen = 0
        self.jdict = []
        self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])

    def get_desc(self):
        """Return a formatted string summarizing class metrics of YOLO model."""
        return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")

    def postprocess(self, preds):
        """Apply Non-maximum suppression to prediction outputs."""
        return ops.non_max_suppression(
            preds,
            self.args.conf,
            self.args.iou,
            labels=self.lb,
            multi_label=True,
            agnostic=self.args.single_cls,
            max_det=self.args.max_det,
        )

    def _prepare_batch(self, si, batch):
        """Prepares a batch of images and annotations for validation."""
        idx = batch["batch_idx"] == si
        cls = batch["cls"][idx].squeeze(-1)
        bbox = batch["bboxes"][idx]
        ori_shape = batch["ori_shape"][si]
        imgsz = batch["img"].shape[2:]
        ratio_pad = batch["ratio_pad"][si]
        if len(cls):
            bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]  # target boxes
            ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad)  # native-space labels
        return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}

    def _prepare_pred(self, pred, pbatch):
        """Prepares a batch of images and annotations for validation."""
        predn = pred.clone()
        ops.scale_boxes(
            pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]
        )  # native-space pred
        return predn

    def update_metrics(self, preds, batch):
        """Metrics."""
        for si, pred in enumerate(preds):
            self.seen += 1
            npr = len(pred)
            stat = dict(
                conf=torch.zeros(0, device=self.device),
                pred_cls=torch.zeros(0, device=self.device),
                tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
            )
            pbatch = self._prepare_batch(si, batch)
            cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
            nl = len(cls)
            stat["target_cls"] = cls
            stat["target_img"] = cls.unique()
            if npr == 0:
                if nl:
                    for k in self.stats.keys():
                        self.stats[k].append(stat[k])
                    if self.args.plots:
                        self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
                continue

            # Predictions
            if self.args.single_cls:
                pred[:, 5] = 0
            predn = self._prepare_pred(pred, pbatch)
            stat["conf"] = predn[:, 4]
            stat["pred_cls"] = predn[:, 5]

            # Evaluate
            if nl:
                stat["tp"] = self._process_batch(predn, bbox, cls)
                if self.args.plots:
                    self.confusion_matrix.process_batch(predn, bbox, cls)
            for k in self.stats.keys():
                self.stats[k].append(stat[k])

            # Save
            if self.args.save_json:
                self.pred_to_json(predn, batch["im_file"][si])
            if self.args.save_txt:
                file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
                self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)

    def finalize_metrics(self, *args, **kwargs):
        """Set final values for metrics speed and confusion matrix."""
        self.metrics.speed = self.speed
        self.metrics.confusion_matrix = self.confusion_matrix

    def get_stats(self):
        """Returns metrics statistics and results dictionary."""
        stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
        self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=self.nc)
        self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
        stats.pop("target_img", None)
        if len(stats) and stats["tp"].any():
            self.metrics.process(**stats)
        return self.metrics.results_dict

    def print_results(self):
        """Prints training/validation set metrics per class."""
        pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys)  # print format
        LOGGER.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
        if self.nt_per_class.sum() == 0:
            LOGGER.warning(f"WARNING ‚ö†ÔłŹ no labels found in {self.args.task} set, can not compute metrics without labels")

        # Print results per class
        if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
            for i, c in enumerate(self.metrics.ap_class_index):
                LOGGER.info(
                    pf % (self.names[c], self.nt_per_image[c], self.nt_per_class[c], *self.metrics.class_result(i))
                )

        if self.args.plots:
            for normalize in True, False:
                self.confusion_matrix.plot(
                    save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
                )

    def _process_batch(self, detections, gt_bboxes, gt_cls):
        """
        Return correct prediction matrix.

        Args:
            detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
                Each detection is of the format: x1, y1, x2, y2, conf, class.
            labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
                Each label is of the format: class, x1, y1, x2, y2.

        Returns:
            (torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
        """
        iou = box_iou(gt_bboxes, detections[:, :4])
        return self.match_predictions(detections[:, 5], gt_cls, iou)

    def build_dataset(self, img_path, mode="val", batch=None):
        """
        Build YOLO Dataset.

        Args:
            img_path (str): Path to the folder containing images.
            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
        """
        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)

    def get_dataloader(self, dataset_path, batch_size):
        """Construct and return dataloader."""
        dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
        return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1)  # return dataloader

    def plot_val_samples(self, batch, ni):
        """Plot validation image samples."""
        plot_images(
            batch["img"],
            batch["batch_idx"],
            batch["cls"].squeeze(-1),
            batch["bboxes"],
            paths=batch["im_file"],
            fname=self.save_dir / f"val_batch{ni}_labels.jpg",
            names=self.names,
            on_plot=self.on_plot,
        )

    def plot_predictions(self, batch, preds, ni):
        """Plots predicted bounding boxes on input images and saves the result."""
        plot_images(
            batch["img"],
            *output_to_target(preds, max_det=self.args.max_det),
            paths=batch["im_file"],
            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
            names=self.names,
            on_plot=self.on_plot,
        )  # pred

    def save_one_txt(self, predn, save_conf, shape, file):
        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
        gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
        for *xyxy, conf, cls in predn.tolist():
            xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
            with open(file, "a") as f:
                f.write(("%g " * len(line)).rstrip() % line + "\n")

    def pred_to_json(self, predn, filename):
        """Serialize YOLO predictions to COCO json format."""
        stem = Path(filename).stem
        image_id = int(stem) if stem.isnumeric() else stem
        box = ops.xyxy2xywh(predn[:, :4])  # xywh
        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
        for p, b in zip(predn.tolist(), box.tolist()):
            self.jdict.append(
                {
                    "image_id": image_id,
                    "category_id": self.class_map[int(p[5])]
                    + (1 if self.is_lvis else 0),  # index starts from 1 if it's lvis
                    "bbox": [round(x, 3) for x in b],
                    "score": round(p[4], 5),
                }
            )

    def eval_json(self, stats):
        """Evaluates YOLO output in JSON format and returns performance statistics."""
        if self.args.save_json and (self.is_coco or self.is_lvis) and len(self.jdict):
            pred_json = self.save_dir / "predictions.json"  # predictions
            anno_json = (
                self.data["path"]
                / "annotations"
                / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
            )  # annotations
            pkg = "pycocotools" if self.is_coco else "lvis"
            LOGGER.info(f"\nEvaluating {pkg} mAP using {pred_json} and {anno_json}...")
            try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
                for x in pred_json, anno_json:
                    assert x.is_file(), f"{x} file not found"
                check_requirements("pycocotools>=2.0.6" if self.is_coco else "lvis>=0.5.3")
                if self.is_coco:
                    from pycocotools.coco import COCO  # noqa
                    from pycocotools.cocoeval import COCOeval  # noqa

                    anno = COCO(str(anno_json))  # init annotations api
                    pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
                    val = COCOeval(anno, pred, "bbox")
                else:
                    from lvis import LVIS, LVISEval

                    anno = LVIS(str(anno_json))  # init annotations api
                    pred = anno._load_json(str(pred_json))  # init predictions api (must pass string, not Path)
                    val = LVISEval(anno, pred, "bbox")
                val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # images to eval
                val.evaluate()
                val.accumulate()
                val.summarize()
                if self.is_lvis:
                    val.print_results()  # explicitly call print_results
                # update mAP50-95 and mAP50
                stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = (
                    val.stats[:2] if self.is_coco else [val.results["AP50"], val.results["AP"]]
                )
            except Exception as e:
                LOGGER.warning(f"{pkg} unable to run: {e}")
        return stats

__init__(dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None)

Inicializa o modelo de dete√ß√£o com as vari√°veis e defini√ß√Ķes necess√°rias.

Código fonte em ultralytics/models/yolo/detect/val.py
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
    """Initialize detection model with necessary variables and settings."""
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
    self.nt_per_class = None
    self.nt_per_image = None
    self.is_coco = False
    self.is_lvis = False
    self.class_map = None
    self.args.task = "detect"
    self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
    self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
    self.niou = self.iouv.numel()
    self.lb = []  # for autolabelling

build_dataset(img_path, mode='val', batch=None)

Constrói o conjunto de dados YOLO .

Par√Ęmetros:

Nome Tipo Descrição Predefinição
img_path str

Caminho para a pasta que contém as imagens.

necess√°rio
mode str

train ou val os utilizadores podem personalizar diferentes aumentos para cada modo.

'val'
batch int

Tamanho dos lotes, isto é para rect. Predefine-se como Nenhum.

None
Código fonte em ultralytics/models/yolo/detect/val.py
def build_dataset(self, img_path, mode="val", batch=None):
    """
    Build YOLO Dataset.

    Args:
        img_path (str): Path to the folder containing images.
        mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
        batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
    """
    return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)

eval_json(stats)

Avalia a saída YOLO no formato JSON e devolve estatísticas de desempenho.

Código fonte em ultralytics/models/yolo/detect/val.py
def eval_json(self, stats):
    """Evaluates YOLO output in JSON format and returns performance statistics."""
    if self.args.save_json and (self.is_coco or self.is_lvis) and len(self.jdict):
        pred_json = self.save_dir / "predictions.json"  # predictions
        anno_json = (
            self.data["path"]
            / "annotations"
            / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
        )  # annotations
        pkg = "pycocotools" if self.is_coco else "lvis"
        LOGGER.info(f"\nEvaluating {pkg} mAP using {pred_json} and {anno_json}...")
        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            for x in pred_json, anno_json:
                assert x.is_file(), f"{x} file not found"
            check_requirements("pycocotools>=2.0.6" if self.is_coco else "lvis>=0.5.3")
            if self.is_coco:
                from pycocotools.coco import COCO  # noqa
                from pycocotools.cocoeval import COCOeval  # noqa

                anno = COCO(str(anno_json))  # init annotations api
                pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
                val = COCOeval(anno, pred, "bbox")
            else:
                from lvis import LVIS, LVISEval

                anno = LVIS(str(anno_json))  # init annotations api
                pred = anno._load_json(str(pred_json))  # init predictions api (must pass string, not Path)
                val = LVISEval(anno, pred, "bbox")
            val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # images to eval
            val.evaluate()
            val.accumulate()
            val.summarize()
            if self.is_lvis:
                val.print_results()  # explicitly call print_results
            # update mAP50-95 and mAP50
            stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = (
                val.stats[:2] if self.is_coco else [val.results["AP50"], val.results["AP"]]
            )
        except Exception as e:
            LOGGER.warning(f"{pkg} unable to run: {e}")
    return stats

finalize_metrics(*args, **kwargs)

Define os valores finais para a velocidade da métrica e a matriz de confusão.

Código fonte em ultralytics/models/yolo/detect/val.py
def finalize_metrics(self, *args, **kwargs):
    """Set final values for metrics speed and confusion matrix."""
    self.metrics.speed = self.speed
    self.metrics.confusion_matrix = self.confusion_matrix

get_dataloader(dataset_path, batch_size)

Constrói e devolve o dataloader.

Código fonte em ultralytics/models/yolo/detect/val.py
def get_dataloader(self, dataset_path, batch_size):
    """Construct and return dataloader."""
    dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
    return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1)  # return dataloader

get_desc()

Devolve uma cadeia de caracteres formatada que resume as métricas de classe do modelo YOLO .

Código fonte em ultralytics/models/yolo/detect/val.py
def get_desc(self):
    """Return a formatted string summarizing class metrics of YOLO model."""
    return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")

get_stats()

Devolve as estatísticas de métricas e o dicionário de resultados.

Código fonte em ultralytics/models/yolo/detect/val.py
def get_stats(self):
    """Returns metrics statistics and results dictionary."""
    stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
    self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=self.nc)
    self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
    stats.pop("target_img", None)
    if len(stats) and stats["tp"].any():
        self.metrics.process(**stats)
    return self.metrics.results_dict

init_metrics(model)

Inicializa as métricas de avaliação para YOLO.

Código fonte em ultralytics/models/yolo/detect/val.py
def init_metrics(self, model):
    """Initialize evaluation metrics for YOLO."""
    val = self.data.get(self.args.split, "")  # validation path
    self.is_coco = isinstance(val, str) and "coco" in val and val.endswith(f"{os.sep}val2017.txt")  # is COCO
    self.is_lvis = isinstance(val, str) and "lvis" in val and not self.is_coco  # is LVIS
    self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(len(model.names)))
    self.args.save_json |= (self.is_coco or self.is_lvis) and not self.training  # run on final val if training COCO
    self.names = model.names
    self.nc = len(model.names)
    self.metrics.names = self.names
    self.metrics.plot = self.args.plots
    self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf)
    self.seen = 0
    self.jdict = []
    self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])

plot_predictions(batch, preds, ni)

Traça caixas delimitadoras previstas nas imagens de entrada e guarda o resultado.

Código fonte em ultralytics/models/yolo/detect/val.py
def plot_predictions(self, batch, preds, ni):
    """Plots predicted bounding boxes on input images and saves the result."""
    plot_images(
        batch["img"],
        *output_to_target(preds, max_det=self.args.max_det),
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_pred.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )  # pred

plot_val_samples(batch, ni)

Coloca amostras de imagens de validação.

Código fonte em ultralytics/models/yolo/detect/val.py
def plot_val_samples(self, batch, ni):
    """Plot validation image samples."""
    plot_images(
        batch["img"],
        batch["batch_idx"],
        batch["cls"].squeeze(-1),
        batch["bboxes"],
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_labels.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )

postprocess(preds)

Aplica a supressão não máxima às saídas de previsão.

Código fonte em ultralytics/models/yolo/detect/val.py
def postprocess(self, preds):
    """Apply Non-maximum suppression to prediction outputs."""
    return ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        labels=self.lb,
        multi_label=True,
        agnostic=self.args.single_cls,
        max_det=self.args.max_det,
    )

pred_to_json(predn, filename)

Serializa as previs√Ķes de YOLO para o formato COCO json.

Código fonte em ultralytics/models/yolo/detect/val.py
def pred_to_json(self, predn, filename):
    """Serialize YOLO predictions to COCO json format."""
    stem = Path(filename).stem
    image_id = int(stem) if stem.isnumeric() else stem
    box = ops.xyxy2xywh(predn[:, :4])  # xywh
    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
    for p, b in zip(predn.tolist(), box.tolist()):
        self.jdict.append(
            {
                "image_id": image_id,
                "category_id": self.class_map[int(p[5])]
                + (1 if self.is_lvis else 0),  # index starts from 1 if it's lvis
                "bbox": [round(x, 3) for x in b],
                "score": round(p[4], 5),
            }
        )

preprocess(batch)

Pré-processa o lote de imagens para o treino YOLO .

Código fonte em ultralytics/models/yolo/detect/val.py
def preprocess(self, batch):
    """Preprocesses batch of images for YOLO training."""
    batch["img"] = batch["img"].to(self.device, non_blocking=True)
    batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
    for k in ["batch_idx", "cls", "bboxes"]:
        batch[k] = batch[k].to(self.device)

    if self.args.save_hybrid:
        height, width = batch["img"].shape[2:]
        nb = len(batch["img"])
        bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
        self.lb = (
            [
                torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
                for i in range(nb)
            ]
            if self.args.save_hybrid
            else []
        )  # for autolabelling

    return batch

print_results()

Imprime as métricas do conjunto de treinamento/validação por classe.

Código fonte em ultralytics/models/yolo/detect/val.py
def print_results(self):
    """Prints training/validation set metrics per class."""
    pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys)  # print format
    LOGGER.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
    if self.nt_per_class.sum() == 0:
        LOGGER.warning(f"WARNING ‚ö†ÔłŹ no labels found in {self.args.task} set, can not compute metrics without labels")

    # Print results per class
    if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
        for i, c in enumerate(self.metrics.ap_class_index):
            LOGGER.info(
                pf % (self.names[c], self.nt_per_image[c], self.nt_per_class[c], *self.metrics.class_result(i))
            )

    if self.args.plots:
        for normalize in True, False:
            self.confusion_matrix.plot(
                save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
            )

save_one_txt(predn, save_conf, shape, file)

Guarda as detec√ß√Ķes de YOLO num ficheiro txt em coordenadas normalizadas num formato espec√≠fico.

Código fonte em ultralytics/models/yolo/detect/val.py
def save_one_txt(self, predn, save_conf, shape, file):
    """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
    for *xyxy, conf, cls in predn.tolist():
        xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
        with open(file, "a") as f:
            f.write(("%g " * len(line)).rstrip() % line + "\n")

update_metrics(preds, batch)

Métricas.

Código fonte em ultralytics/models/yolo/detect/val.py
def update_metrics(self, preds, batch):
    """Metrics."""
    for si, pred in enumerate(preds):
        self.seen += 1
        npr = len(pred)
        stat = dict(
            conf=torch.zeros(0, device=self.device),
            pred_cls=torch.zeros(0, device=self.device),
            tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
        )
        pbatch = self._prepare_batch(si, batch)
        cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
        nl = len(cls)
        stat["target_cls"] = cls
        stat["target_img"] = cls.unique()
        if npr == 0:
            if nl:
                for k in self.stats.keys():
                    self.stats[k].append(stat[k])
                if self.args.plots:
                    self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
            continue

        # Predictions
        if self.args.single_cls:
            pred[:, 5] = 0
        predn = self._prepare_pred(pred, pbatch)
        stat["conf"] = predn[:, 4]
        stat["pred_cls"] = predn[:, 5]

        # Evaluate
        if nl:
            stat["tp"] = self._process_batch(predn, bbox, cls)
            if self.args.plots:
                self.confusion_matrix.process_batch(predn, bbox, cls)
        for k in self.stats.keys():
            self.stats[k].append(stat[k])

        # Save
        if self.args.save_json:
            self.pred_to_json(predn, batch["im_file"][si])
        if self.args.save_txt:
            file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
            self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)